September 2018
Intermediate to advanced
472 pages
12h 2m
English
book
Python Data Science Essentials - Third Edition
by Alberto Boschetti, Luca Massaron, Pietro Marinelli, Matteo Malosetti
Content preview from Python Data Science Essentials - Third Edition
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Regression
In tasks where you have to predict real numbers or regression, many error measures are derived from Euclidean algebra:
- Mean absolute error or MAE: This is the mean L1 norm of the difference vector between the predicted and real values:
In: from sklearn.metrics import mean_absolute_error mean_absolute_error([1.0, 0.0, 0.0], [0.0, 0.0, -1.0]) Out: 0.66666666666666663
- Mean squared error or MSE: This is the mean L2 norm of the difference vector between the predicted and real values:
In: from sklearn.metrics import mean_squared_error mean_squared_error([-10.0, 0.0, 0.0], [0.0, 0.0, 0.0])Out: 33.333333333333
- R2 score: R2 is also known as the coefficient of determination. In a nutshell, R2 determines how good a linear fit there ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.